You are using pip version 10.0.1, however version 19.0.3 is available. You should consider upgrading via the 'python -m pip install -upgrade pip' command. C:Usersgaroopython -m pip install -upgrade pip Here is a picture of what I put into the command line. Pip is the standard package manager for Python. It enables the installation and management of third party packages that provide features and functionality not contained in the Python standard library. Newer versions of Python (Python 2 = v2.7.9 or Python 3 = v3.4) come prepackaged with pip by default.
Introduction
This is a quickstart guide to Python Packaging with a particular focus on the creation of a PyPI package, which will enable users to “pip install” the package. The document is broken down into sections so that readers may easily skips parts of the process they are already familiar with. All but the final section (Uploading to PyPI), can be undertaken as an exercise to understand Python packaging and test the process, without publishing a package on the formal PyPI distribution.
For a more detailed reference on package creation, see the official Python Packaging Authority (PyPA) website.
Note: PyPI should be pronounced “pie P I” to avoid confusion with pypy (a Python implementation).
What is pip?
pip is a package management system, specifically designed for installing Python packages from from the internet hosted Python Package Index (commonly known as PyPI). It is the most common way to install Python packages. E.g.
The package can now be imported in Python scripts. You may need to run as sudo if you have root privileges, or append
--user
to install under your home directory (often this will be under $HOME/.local).Note: pip3 is used to install Python3 packages, however in some environments the command pip may point to pip3, just as python may point to Python3. You can use
which pip
to check this. For this document, examples will show the command simply as pip.Tip: To download a specific version of a package:
Tip: To find out what versions are available:
This is essentially trying to install a version that does not exist and causes pip to list available versions.
Tip: To see what version is currently installed:
Package information, including install location, can be obtained by running the Python interpreter:
Installing pip is easy: https://pip.pypa.io/en/stable/installing
Creating a Python package
This article gives an overview of how to create an installable Python package.
Note on Ambiguity: The term package can refer to an installable python package within a project (a directory containing an __init__.py file). It can also mean a distribution package which refers to the entire distributed part of the project (as in a source distribution - or “tarball”). Such a package may consist of multiple python package/sub-packages. In most cases the context should be sufficient to make the distinction.
A Python project will consist of a root directory with the name of the project. Somewhere inside this will be included a directory which will constitute the main installable package. Most often this has the same name as the project (This is not compulsory but makes things a bit simpler. Inside that package directory, alongside your python files, create a file called __init__.py. This file can be empty, and it denotes the directory as a python package. When you pip install, this directory will be installed and become importable.
E.g. A simple project may have this structure:
At the root directory, you will need a setup.py file, which will govern the installation of your package. The setuptools package is recommended for this (the in-built distutils is an older alternative).
The main requirement in setup.py is to call the setup routine, providing project information as keyword arguments. A lot of information can be provided, but the following is a minimalist example.
Further information on setup options can be found at: PyPA packaging instructions and yet more detailed and up to date information at: The setuptools command reference:
The classifiers are not functional, they are for documentation, and will be listed on the PYPI page, once uploaded. It is conventional to include the Python versions supported in this release. A complete list of classifiers is available at:PyPI classifiers list
Having created a setup.py, test the install with pip. In root dir:
This is recommended in place of the default
python setup.py install
which uses easy_install. If you have an existing install, and want to ensure package and dependencies are updated use --upgrade
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To uninstall (use package name):
![Python install module Python install module](/uploads/1/3/7/5/137510991/701639707.png)
Note: A reliable clean uninstall is one advantage of using setuptools over distutils.
It is worth noting that the version in your setup.py will not provide the package attribute __version__. A common place to provide this along with other meta-data for the package is inside the __init__.py. This is run whenever the module is imported. E.g: __init__.py may contain:
If you now pip install again and run the Python interpreter you should be able to access these variables:
This does create the problem of having two places holding the version, which must also match any release tags created (eg. in git). Various approaches exist for using a single version number. See https://packaging.python.org/guides/single-sourcing-package-version
If you wish to create sub-packages, these should ideally be directories inside the main package (Re-mapping from other locations is possible using the package_dir argument in setup but this can cause a problem with develop installs. The sub-packages also require an __init__.py in the directory.
Creating a source distribution
It is recommended that all Python projects provide a source distribution.
PyPI has certain required meta-data that the setup.py should provide. To quickly check if your project has this data use:
If nothing is reported your package is acceptable.
Create a source distribution. From your root directory:
This creates a dist/ directory containing a compressed archive of the package (e.g.
<PACKAGE_NAME>-<VERSION>
.tar.gz in Linux). This file is your source distribution. If it does not automatically contain what you want, then you might consider using a MANIFEST file (see https://docs.python.org/distutils/sourcedist).Note: A
<PACKAGE_NAME>
.egg-info directory will also be created in your root directory containing meta-data about your distribution. This can safely be deleted if it is not wanted (despite the extension, this is generated even though you have not built an egg format package).Creating a wheel distribution
Optionally you may create a wheel distribution. This is a built distribution for the current platform. Wheels should be used in place of the older egg format. Bear in mind, any extensions will be built for the given platform and as such this must be consistent with any other project dependencies. Wheels will speed up installation if you have compiled code extensions as the build step is not required.
If you do not have the wheel package you can pip install it.
There are different types of wheels. However, if your project is pure python and python2/3 compatible create a universal wheel:
If it is not python2/3 compatible or contains compiled extensions just use:
The installable wheel will be created under the dist/ directory. A build directory will also be created with the built code.
Further details for building wheels can be found here: https://packaging.python.org/tutorials/distributing-packages
Testing and Publishing package on PyPI
Distributing the package on PyPI will enable anyone on-line to pip install the package.
First you must set up an account on PyPI. If you are going to test your package on the PyPI test site you will need to set up an account there also. This is easy.
Create an account on PYPI:Go to: https://pypi.python.org and select Register. Follow instructions.
Create an account on testpypi:Go to: https://testpypi.python.org and select Register. Follow instructions.
You will also need a version number. Semantic versioning is recommended (see https://semver.org for details). The standard starting version for a project in development is 0.1.0.
The best approach to uploading to PyPI is to use twine.
IMPORTANT: First you can test your upload using the PyPI test site. It is highly recommended that you do this and test installing your package as below. NOTE: Once you upload a package to PYPI it is possible to remove it, but you cannot upload another package with the same version number – this breaks the version contract. It is therefore, especially prudent to test with testpypi first. Note that anything you put on testpypi should be considered disposable as the site regularly prunes content.
Uploading to testpypi
This section shows how to upload a source distribution of your package.
Further documentation at: https://packaging.python.org/guides/using-testpypiNote: This link includes the option of using a pypirc file to abbreviate some of the command lines below.
Python Pip Upgrade
A source distribution provides everything needed to build/install the package on any supported platform. Testsuites, documentation and supporting data can also be included.
You can now upload your package to testpypi as follows. Assuming your source distribution under dist/ is called pyexample-0.1.0.tar.gz:
Alternatively, the following line will upload all your generated distrbutions under the dist/ directory. This may be used if you create wheels (see below) in addition to a source distribution.
You will be requested to give your username and password for your testpypi account.
Option: You have the option to digitally sign your package when you upload. You will need a gpg key set up to do this. It should be noted, however, that pip does not currently check gpg signatures when installing - this has to be done manually.
To digitally sign using your gpg key (e.g. for package pyexample at version 0.1.0):
A file pyexample-0.1.0.tar.gz.asc will be created. Now upload:
Note:
--detach-sign
means you are writing the signature into a separate file *.ascThe package should now be uploaded to: https://testpypi.python.org/pypi
Note how the info/classifiers you supplied in setup.py are shown on the page.
You can now test pip install from the command line. E.g. To install package pyexample into your user install space:
Uploading to PyPI
Once you are happy with the repository in testpyi, uploading to PYPI will be the same command line process, but without having to specify the url arguments. For example, the steps above would simply become:
E.g. To upload all distributions created under dist/
E.g. To upload the source distribution with a gpg signature:
Install Pip
Owlr ip camera viewer. You package should now be uploaded to: https://pypi.python.org/pypi
The package should pip install. E.g:
It is also recommended that you use virtual environments to test installing dependencies from scratch and for trying out different python versions. Check required flags to ensure your environment is isolated. E.g. For Virtualenv use the flag
--no-site-packages
. For Conda, set the environment variable export PYTHONNOUSERSITE=1
before activating you environment. Packages that are explicitly linked through PYTHONPATH will still be found however.Downloading tarball without install
To test downloading a source distribution (no install) with dependencies:
Or just the package without dependencies:
Downloading the source distribution is a good way to check that it includes what you want by default. If not, then consider adding a MANIFEST file, which instructs setuptools what to include in the source distribution.
Example projects
pyexample:
A small sample project using numpy and mpi4py (used as example above).
Location:
Note: To run the mpi4py test use at least 2 processors:
mpiexec -np 2 python module_mpi4py_1.py
libEnsemble:
An Argonne project for running ensembles of calculations.
Location:
Related content includes:
- setup.py includes mapping a different source directory structure topackages and sub-packages using the package_dir setup argument.
- Use of a MANIFEST file to specify source distribution.
Feedback
Any feedback/corrections/additions are welcome:
- If this was helpful, please leave a star on the github page.
- Leave a comment below.
- Email: [email protected]
- Or fork on github and make a pull request
Back to contents page
Unfortunately, issues can arise when conda and pip are used together to create an environment, especially when the tools are used back-to-back multiple times, establishing a state that can be hard to reproduce. Most of these issues stem from that fact that conda, like other package managers, has limited abilities to control packages it did not install. Running conda after pip has the potential to overwrite and potentially break packages installed via pip. Similarly, pip may upgrade or remove a package which a conda-installed package requires. In some cases these breakages are cosmetic, where a few files are present that should have been removed, but in other cases the environment may evolve into an unusable state.
There are a few steps which can be used to avoid broken environments when using conda and pip together. One surefire method is to only use conda packages. If software is needed which is not available as a conda package, conda build can be used to create packages for said software. For projects available on PyPI, the conda skeleton command (which is part of conda-build) frequently produces a recipe which can be used create a conda package with little or no modifications.
Creating conda packages for all additional software needed is a reliably safe method for putting together a data science environment but can be a burden if the environment involves a large number of packages which are only available on PyPI. In these cases, using pip only after all other requirements have been installed via conda is the safest practice. Additionally, pip should be run with the “--upgrade-strategy only-if-needed” argument to prevent packages installed via conda from being upgraded unnecessarily. This is the default when running pip but it should not be changed.
If there is an expectation to install software using pip along-side conda packages it is a good practice to do this installation into a purpose-built conda environment to protect other environments from any modifications that pip might make. Conda environments are isolated from each other and allow different versions of packages to be installed. In conda environments, hard links are used when possible rather than copying files to save space. If a similar set of packages are installed, each new conda environment will require only a small amount of additional disk space. Many users rely on simply the “root” conda environment that is created by installing either Anaconda or Miniconda. If this environment becomes cluttered with a mix of pip and conda installs, it is much harder to recover. On the other hand, creating separate conda environments allows you to delete and recreate environments readily, without risking your core conda functionality.
Once pip is used to install software into a conda environment, conda will be unaware of these changes and may make modifications that would break the environment. Rather than running conda, pip and then conda again, a more reliable method is to create a new environment with the combined conda requirements and then run pip. This new environment can be tested before removing the old one. Again, it is primarily the “statefulness” of pip that causes problems - the more state that exists because of the order of installation of packages, the harder it will be to keep things working.
For environments that will be recreated often, it is a good practice to store the conda and pip package requirements in text files. Package requirements can be provided to conda via the --file argument and pip via the -r or --requirement. A single file containing both conda and pip requirements can be exported or provided to the conda env command to control an environment. Both of these methods have the benefit that the files describing the environment can be checked into a version control system and shared with others.
In summary, when combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip. When appropriate conda and pip requirements should be stored in text files.
We at Anaconda are keenly aware of the difficulties in combining pip and conda. We want the process of setting up data science environments to be as easy as possible. That is why we have been adding new features to the next version of conda to simplify this process. While still in beta, conda 4.6.0 allows conda to consider pip installed packages and either replace these packages as needed or fulfill dependencies with the existing package. We are still testing these new features but expect the interactions between conda and pip to be greatly improved in the near future.
Best Practices Checklist
Use pip only after conda
- install as many requirements as possible with conda, then use pip
- pip should be run with --upgrade-strategy only-if-needed (the default)
- Do not use pip with the --user argument, avoid all “users” installs
Use conda environments for isolation
- create a conda environment to isolate any changes pip makes
- environments take up little space thanks to hard links
- care should be taken to avoid running pip in the “root” environment
Recreate the environment if changes are needed
- once pip has been used conda will be unaware of the changes
- to install additional conda packages it is best to recreate the environment
Store conda and pip requirements in text files
- package requirements can be passed to conda via the --file argument
- pip accepts a list of Python packages with -r or --requirements
- conda env will export or create environments based on a file with conda and pip requirements
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